The Commercial Determinants of Men’s Health Promotion: A Case Study of Gambling, Nonnies, and Athleisurewear
Bibliographic record
Abstract
Although the social determinants of health have guided equity work with the tailoring of men's health promotion programs, the role of, and potential for, the commercial determinants of health in those interventions is rarely addressed and poorly understood. While four commercial products, tobacco, alcohol, ultra-processed food, and fossil fuels, account for more than a third of global deaths, there is a need to recognize that consumer goods industries can make both positive and negative contributions to health. This article begins much-needed discussions about what we might learn from, and strategically tap in the commercial sector to seed, scale, and sustain men's health promotion programs. Three case studies, online sports betting, beer and the rise of the nonny, and athleisurewear, are discussed. Connections between online sports betting and masculinities explain young men's disproportionate involvement and gambling addictions with recommendations to legislate an end to gambling advertisements and de-incentivize industry profiteering through penalties and higher taxes. Regarding beer and the rise of the nonny, brewers have innovated with non-alcoholic beer based on shifting consumption patterns and masculinities in their core market-men. The nonny reminds health promoters to know their end-user's values and behaviors to bolster program acceptability. Detailing Under Armour and Lululemon, two highly gendered but diversifying athleisurewear brands, the complexities of, and potential for, leveraging public health and industry collaborations are underscored. Taken together, the article findings suggest men's health promoters should rigorously explore tapping key commercial entities and tax revenues to advance the health of men and their communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".